> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pylar.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Feature Flag Manager

> Build an AI agent that manages feature rollouts, analyzes flag performance, and recommends rollback decisions

## Overview

A Feature Flag Manager powered by Pylar monitors feature flag performance, analyzes user impact, and provides data-driven recommendations for rollouts and rollbacks.

## What the Agent Needs to Accomplish

The agent must:

* Monitor feature flag status
* Analyze flag performance metrics
* Track error rates and user impact
* Recommend rollback decisions
* Optimize rollout percentages
* Track feature flag effectiveness

## How Pylar Helps

Pylar enables the agent by:

* **Unified Flag View**: Combining flag data, error rates, and user metrics
* **Real-time Monitoring**: Querying current flag performance
* **Impact Analysis**: Automated impact calculations
* **Decision Support**: Data-driven rollout recommendations

```mermaid theme={null}
graph LR
    subgraph "Data Sources"
        FLAGS[Feature Flags]
        ERRORS[Error Rates]
        METRICS[User Metrics]
    end
    
    PYLAR[Pylar<br/>Unified Views]
    
    TOOLS[MCP Tools<br/>monitor_flags<br/>analyze_impact<br/>recommend_rollback]
    
    AGENT[Flag Manager]
    TEAM[Engineering Team]
    
    FLAGS --> PYLAR
    ERRORS --> PYLAR
    METRICS --> PYLAR
    PYLAR --> TOOLS
    TOOLS --> AGENT
    AGENT --> TEAM
    
    style PYLAR fill:#FF4017,stroke:#CC3300,color:#fff
```

## Without Pylar vs With Pylar

### Without Pylar

**Challenges**:

* ❌ Manual flag monitoring
* ❌ Complex error correlation
* ❌ Time-consuming impact analysis
* ❌ Limited real-time visibility

**Implementation Complexity**: \~3-4 weeks

### With Pylar

**Benefits**:

* ✅ Automated flag monitoring
* ✅ Real-time performance tracking
* ✅ Instant impact analysis
* ✅ Clear rollback recommendations

**Implementation Complexity**: \~4-5 hours

## Step-by-Step Implementation

### Step 1: Connect Data Sources

1. **Connect Feature Flag Platform** (LaunchDarkly, Split.io)
2. **Connect Error Tracking** (Sentry, Bugsnag)
3. **Connect Product Analytics** (User behavior, engagement)

### Step 2: Create Flag Views

**Feature Flag Performance View**:

```sql theme={null}
CREATE VIEW flag_performance AS
SELECT 
  f.flag_name,
  f.enabled_percentage,
  f.status,
  COUNT(DISTINCT u.user_id) as users_affected,
  e.error_count,
  e.error_rate,
  a.engagement_score,
  -- Risk score
  (e.error_rate * 50 + 
   CASE WHEN a.engagement_score < 50 THEN 30 ELSE 0 END +
   CASE WHEN f.enabled_percentage > 50 AND e.error_rate > 0.05 THEN 20 ELSE 0 END) as risk_score
FROM feature_flags.flags f
LEFT JOIN errors.error_summary e ON f.flag_name = e.flag_name
LEFT JOIN analytics.user_engagement a ON f.flag_name = a.flag_name
GROUP BY f.flag_name, f.enabled_percentage, f.status, e.error_count, e.error_rate, a.engagement_score;
```

### Step 3: Create MCP Tools

**Tool 1: Monitor Feature Flag**

* `monitor_feature_flag(flag_name: string)`

**Tool 2: Analyze Flag Performance**

* `analyze_flag_performance(flag_name: string, hours_back: number)`

**Tool 3: Recommend Rollback**

* `recommend_rollback(flag_name: string, threshold: number)`

**Tool 4: Optimize Rollout**

* `optimize_rollout(flag_name: string, target_percentage: number)`

## Example Agent Interactions

**User**: "Should we rollback the new checkout feature?"

**Agent**: "Feature Flag Analysis: 'new\_checkout'

* Status: 35% enabled
* Error Rate: 2.1% (Threshold: 1%)
* User Impact: 1,200 users affected
* Engagement: -15% vs baseline
* Risk Score: 68 (High)

Recommendation: ROLLBACK - Error rate exceeds threshold and engagement is declining"

## Outcomes

* **Incident Detection**: 60% faster detection
* **Rollback Decisions**: 50% more accurate decisions
* **User Impact**: 40% reduction in negative impact
* **Feature Success**: 25% improvement in successful rollouts

## Next Steps

* [Product Usage Analyst Example](/examples/product-usage-analyst)
* [Product Feedback Analyzer Example](/examples/product-feedback-analyzer)